3 papers
cs.CR2026
Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning
Hongliang Zhang, Zhongyuan Yu, Guijuan Wang +4
Federated Learning (FL) is vulnerable to backdoor attacks because of its distributed nature in edge computing scenarios. Existing defense methods show limited efficacy as they over…
cs.CR2025
X-PRINT:Platform-Agnostic and Scalable Fine-Grained Encrypted Traffic Fingerprinting
YuKun Zhu, ManYuan Hua, Hai Huang +7
Although encryption protocols such as TLS are widely de-ployed,side-channel metadata in encrypted traffic still reveals patterns that allow application and behavior inference.How-e…
cs.CR2025
PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks
Hongliang Zhang, Jiguo Yu, Fenghua Xu +5
Privacy-Preserving Federated Learning (PPFL) enables multiple clients to collaboratively train models by submitting secreted model updates. Nonetheless, PPFL is vulnerable to data…